PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Analysis of the effects of recycling on process control

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The union of different devices in order to obtain a specific response for a process is commonly called a control system. For a control system, it is necessary to have one or more controllers. Among the most used in the industrial sector are the PID and PI controllers. Next to these controllers is the control software. Scilab is a good example of control software. It is characterized as free code software, with no cost for its acquisition, in addition to having a large computational power and integrated tools, such as Xcos, intended for modeling and simulation. For the union with Scilab, there is Arduino. Such a mixture can be used, for example, to control liquid levels in tanks. In this context, the present work aims to study the tank-level control system based on PID and PI controllers through the union between Scilab and Arduino. Phenomenological models were developed based on closed-loop control (feedback control system) of the process with two tanks not coupled with recycle. Furthermore, for comparison purposes, two approaches were used for each process: one considering the saturation of the manipulated variable and the other without the presence of such saturation. At first, there was a need to implement an anti-windup system. For tuning the controller parameters, the ISE method was used, executed through a programming code developed in Scilab. The parameters found for the two systems were tested on a made-up experimental bench. Therefore, using the block diagrams and the method here called “ISE method”, satisfactory values were obtained for the control parameters. These were ratified in the tests carried out in the experimental module. Level control was achieved with greater prominence for the PI controller since there is one less parameter to be tuned and processed by the system. This controller provided results close to the PID controller for cycles up to 50%. In general, the PI controller showed maximum response deviations smaller than the PID, such as deviations of 1.55 cm and 2.40 cm, respectively, for the case with 75% recycle. It was also clear the influence of the saturation of the manipulated variable on the system response, but not on the tuning of the controller parameterseters.
Słowa kluczowe
Rocznik
Strony
43--55
Opis fizyczny
Bibliogr. 34 poz., rys., tab., wz.
Twórcy
  • State University of Maringa – UEM, Maringa, Paraná, Brazi
  • Itaipu Binacional hydroelectric power plant, Foz do Iguaçu, Paraná, Brazil
  • State University of Maringa – UEM, Maringa, Paraná, Brazi
  • State University of Maringa – UEM, Maringa, Paraná, Brazi
  • State University of Maringa – UEM, Maringa, Paraná, Brazi
Bibliografia
  • 1. Roy, P. & Roy, B.K. (2016). Fractional order PI control applied to level control in coupled two tank MIMO system witch experimental validation. Control Eng. Pract. 48, 119. DOI: 10.1016/j.conengprac.2016.01.002.
  • 2. Kadhim, R.A., Raheem, A.K.K.A. & Gitaffa, S.A.H. (2017). Implementing of liquid tank level control using arduino-labview interfaceing with ultrasonic sensor. Kufa J. Eng. 8, 29.
  • 3. Kittur, J. (2018). Enhancing the controller design skills in the course linear control systems. J. Eng. Educ. Transform. Special Issue. ISSN 2394-1707.
  • 4. Mendes, J., Osório, L. & Araújo, R. (2017). Self-Tuning PID Controllers in Pursuit of Plug and Play Capacity. Control Eng. Pract. 69, 73. DOI: 10.1016/j.conengprac.2017.09.006.
  • 5. Urrego, J.A.R. & Restrepo, N.L.P. (2016). Aplicación de Diseño, simulación, identificación de sistemas e implementación de controladores PID – DIGITROL. Rev. Politécnica. 12, 27. ISSN 2256-5353.
  • 6. Fernandes, J.P.S., Vidal, S.F.S., Marques, M.O., Souza, V.R.S. & Medeiros, L.A. (2019). Identification, control and tuning of non-interactive series tanks. Braz. J. Dev. 5. DOI: 10.34117/bjdv5n10-207.
  • 7. Prusty, S.B., Seshagiri, S., Pati, U.C. & Mahapatra, K.K. (2016). Sliding Mode Control of Coupled Tanks using Conditional Integrators. Indian Control Conference, Indian Institute of technology Hyderabad, (2016).
  • 8. Luz, G.R., Conceição, W.A.S., Jorge, Luiz, M. M., paraíso, P.R. & Andrade, C.M.G. (2010). Dynamic modeling and control of soybean meal drying in a direct rotary dryer. Food Bioprod. Process. 88. DOI: 10.1016/j.fbp.2010.01.008.
  • 9. Pan, H., Wong, H., Kapila, V. & Queiroz, M.S. (2005). Experimental validation of a non linear backstepping liquid level controller for a state coupled two tank system. Control Eng. Pract. 13, 27. DOI: 10.1016/j.conengprac.2003.12.019.
  • 10. Shah, D.H. & Patel, D.M. (2019). Design of sliding mode control for quadruple-tank MIMO process with time delay compensation. J. Process Control 76, 46. DOI: 10.1016/j.jprocont.2019.01.006.
  • 11. Ogunnaike, B.A. & Ray, W.H. Process Dynamics, Modeling, and Control. New York: Oxford University Press, 1994.
  • 12. Wang, J., Shin., P.C., Wu, Y. & Carroll, J.M. (2015). Comparative case studies of open source software peer review practices. Inf. Softw. Technol. 67, 1. DOI: 10.1016/j.infsof.2015.06.002.
  • 13. Verdejo, H. & Becker, C. (2020). The Erratic Implementation of Measuring, Monitoring and Control System (MMCS) in Chile: The crisis on smart meters. Energy Reports 6, 2140. DOI: 10.1016/j.egyr.2020.08.005.
  • 14. Alvaro, R.J., Maria, A.M. & David, F.B. (2018). Level control in system of tanks in interacting mode using Xcos software. Contemp. Eng. Sci. 11, 63. DOI: 10.12988/ces.2018.712206.
  • 15. Mankotia, A. & Shukla, A.K. (2021). IOT based manhole detection and monitoring system using Arduino. Mater. Today Process. 30. DOI: 10.1016/j.matpr.2021.12.264.
  • 16. Omar, H.M. (2018). Enhancing automatic control learning through Arduinobased projects. Eur. J. Eng. Educ. 43, DOI: 10.1080/03043797.2017.1390548.
  • 17. Machado, M.M., Carvalho, A.J., Santos, M.F. & Carvalho, J.R. (2018). Case Study: Level and Temperature Multivariable Control and Design via Arduino through Control Loop Decoupling. 19th International Carpathian Control Conference (ICCC).
  • 18. Veerasamy, V., Wahab, N.I.A., Ramachandran, R., Othman, M.L., Hizam, H., Kumar, J S. & Irudayaraj, A.X.R. Design of single- and multi-loop self-adaptive PID controller using heuristic based recurrent neural network for ALFC of hybrid power system. Expert Syst. Appl. 192, (2022).
  • 19. Briones, O., Alarcón, R., Rojas, J.A. & Sbarbaro, D. (2022). Tuning Generalized Predictive PI controllers for process control applications. ISA Trans. 119, 184. DOI: 10.1016/j.isatra.2021.02.040.
  • 20. Rodriguez, A., Plett, G.L. & Trimboli, M.S. (2018). Improved transfer functions modeling linearized lithium-ion battery-cell internal electrochemical variables. J. Energy Storage 20, 560. DOI: 10.1016/j.est.2018.06.015.
  • 21. Ogata, K. Engenharia de controle moderno. 4ª ed. Prentice-Hall Inc, 2003.
  • 22. Ziegler, J.G. & Nichols, N.B. Optimum Settings For Automatic Controllers. Transactions of the A.S.M.E, 1942. 759–768.
  • 23. Kaistha, N. Liquid level control in a recycle loop. J. Process Control 104 (2021) 11. DOI: 10.1016/j.jprocont.2021.05.014.
  • 24. Grimholt, C., Skogestad, S. (2018). Optimal PI and PID control of first-order plus delay processes and evaluation of the original and improved SIMC rules. J. Process Control 70, 36. DOI: 10.1016/j.jprocont.2018.06.011.
  • 25. Neto, A.F.S., Gomes, F.J. Modeling and Control of a Tito Process System Industrial through the OPC Protocol and FOSS Scilab. Brazilian Soc. Appl. Comput. Mathematics. 4, (2016).
  • 26. Handbook of Chemistry and Physics, CRC press, Ed. 64.
  • 27. Rajamand, S., (2021). Feedback-based control structure for frequency/voltage regulation using the state of electrical vehicle charge station and point estimation method. Sustain. Energy Technol. Assess. 51, 101922. DOI:10.1016/j.seta.2021.101922.
  • 28. Meng, X., Yu, H., Zhang, J., Xu, T., Wu, H. & Yan, K. (2021). Disturbance Observer-Based Feedback Linearization Control for a Quadruple-Tank Liquid Level System. ISA Trans 27, DOI: 10.1016/j.isatra.2021.04.021.
  • 29. Lawrence, N.P., Forbes, M.G., Loewen, P.D., McClement, D.G., Backström, J.U. & Gopaluni, R.B. (2022). Deep reinforcement learning with shallow controllers: An experimental application to PID tuning. Control Eng. Pract. 121, 105046. DOI: 10.1016/j.conengprac.2021.105046.
  • 30. Badu, N.R. & Saikia, L.C. (2022). Optimal location of accurate HVDC and energy storage devices in a deregulated AGC integrated with PWTS considering HPA-ISE as performance index. Eng. Sci. Tech. Inter. J. 33, 101072. DOI: 10.1016/j.jestch.2021.10.004.
  • 31. Bui, P.D.H. & You, S.S. (2022). Dynamics modeling and motion control for high-speed underwater vehicles using H-infinity synthesis with anti-windup compensator. J. Ocean Eng. Sci. 7, 84, DOI: 10.1016/j.joes.2021.07.002.
  • 32. Neto, A.M.S., Damo, T.P. & Coelho, A.A.R. Laboratory Essay with Online Back-calculation Anti-Windup Scheme for a MTG System. IFAC 45, (2012).
  • 33. Rubio, J.F.M., Cuéllar, B.M., Villa, M.V., Rodríguez, D.C. & Sename, Q. (2012). Control of delayed recycling systems with unstable first order forward loop. J. Process Control 22, 729. DOI: 10.1016/j.jprocont.2012.02.002.
  • 34. Samad, T.E. & Annaswamy, A.M. Control in the process industries, The impact of control technology, IEEE Control System Society, 21, (2011).
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ca1d0dda-78a7-436c-ad5a-c935fd94c915
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.